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The graph below shows growth of a $1 invested since March 2004 for a passive, non-levered risk parity portfolio as compared with a 60% equity/40%bond market portfolio. As expected, the risk-parity portfolio lowers downside risk via diversification and maintains a greater return until 2Q2013. The market portfolio closes the gap and then overtakes the risk parity portfolio only due to the extended bull market from 3Q2012 to 1Q2013.

As the growth comparison is based on a passive, non-levered risk parity strategy, a chart of AQR’s Risk Parity strategy is also included as AQR uses both leverage and active management. The inception of this mutual fund is in 2010 so the chart is from October 2010 until August 20, 2013. The reversal in May 2013 markedly stands out in this graph.

Investment in alternative funds that provide absolute and low correlation to equity returns should also provide downside protection and a risk/return structure similar to risk parity strategies. Alternative funds and ETFs have become increasingly popular as a method to provide hedge fund like returns without the 2/20 administrative costs of hedge funds. Many of these funds started trading post-crisis and therefore don’t have a long track record. However, indices such as Hedge Fund Research’s Global Hedge Fund Index (HFRX) may provide a proxy measure for aggregate level performance.

For purposes of this research, two alternative funds were chosen. The first is the Natixis ASG Global Alternatives Fund (Ticker: GAFYX). The second is Goldman Sachs Absolute Return Tracker Fund (Ticker: GARTX). GAFYX is an absolute return fund that seeks to provide capital appreciation consistent with a portfolio of hedge funds. Similarly, GARTX also seeks to provide returns consistent with investment in a basket of hedge funds. Both GAFYX and GARTX began trading in 2008.

Two alternative portfolios were created. Alternative portfolio1 has a 40% allocation to Fixed Income (AGG), 40% allocation to equities (SPY), and 20% to the alternatives fund (GARTX). Alternative portfolio2 has a 40% allocation to Fixed Income (AGG), 40% allocation to equities (SPY), and 20% to the alternatives fund (GAFYX). A 20% allocation to alternatives came through reduction of the 60% equity allocation in the market portfolio. The basis of only decreasing the equity allocation is that, historically, returns from a basket of hedge funds have a higher correlation with equities than bonds . Likewise, a basket of hedge funds also should provide downside protection when applied to equity investments.

Since both GAFYX and GARTX began trading in 2008, comparisons to risk parity portfolios are only from 2008 until August 2013. The first comparison is a Scatter Plot of returns. Correlations of the risk parity to the alternative portfolio returns are extremely high and correlation of the risk parity portfolio to the alternative component GARTX and GAFYX are 0.6 and 0.88.

Interestingly, correlation of the AQR fund (AQRIX) to GARTX and GAFYX are much lower (0.22, 0.38) which points to the effect of active management of a risk parity strategy. The $1Growth charts shows how closely a passive risk parity and alternative portfolio have moved together since 2010.

These graphs again show the large relative valuation drop in an active risk parity strategy (AQRIX) starting in May 2013 as compared to the relative stability to an alternative+ portfolio.

Does this prove that alternative+ portfolios are the new risk parity strategies?

No. However, this does infer that alternative portfolios can provide passive risk parity like returns. In theory, they should also provide downside protection as well. That said, more statistical comparisons and analysis should help to determine if alternative+ portfolios will have the effectiveness and popularity of risk-parity strategies.

Risk Parity strategies have been effective and popular strategies since the early 1990’s as diversification benefits enabled risk parity strategies to make money in most environments. However, the tide may be going out for risk parity strategies as rising interest rate environments impact their ability to use leverage to boost returns.

While investors wonder if this is the ebb of risk parity strategies, the number of alternative mutual funds and ETF/ETNs continues to rise. This raises the following question. If both risk parity and alternative funds provide diversification and downside protection, can allocation to alternative funds provide risk parity like returns? In other words, are alternative funds the new risk parity strategies?

Historically, as shown by the MSCI Barra graph above, risk parity strategies have weathered market downturns and delivered returns above a standard 60% equity/40 % bond market portfolio. However, as interest rates have risen starting in 2Q2013, risk parity strategies have been hit hard. The graph below shows two risk parity strategies, one managed by AQR and the second managed by Invesco. Both funds have taken significant hits since May 2013 with the funds down 6.45% and 3.21% YTD respectively.

The $24.7 trillion Credit Default Swap market[i] is one of the last untapped exchange-traded derivatives markets. The question remains is the market ripe for a CDS futures contract, or as Mark Twain famously put it, “Never Try To Teach A Pig To Sing. It Wastes Your Time And Annoys The Pig.” So, can this pig finally sing?

The InterContinentalExchange (ICE) believes it is the right time and launched a CDS Futures contract (Ticker: WIG) on June 17, 2013. The contract is based on Markit’s liquid CDX.NA.IG 5Y series. The contract is complementary to the current “on the run” OTC traded CDX.NA.IG 5Y swap as the futures contract is priced on the theoretical value of the next CDX.NA.IG series. This “When Issued” structure creates an option valued on the next CDS series. In other words, it is a vehicle to hedge near term macro-economic credit risk (as opposed to immediate term credit risk with an OTC swap).

The chart below shows trading in the Sep 13 CDS future. Although less than impressive, it is important to look back to the introduction on US Treasury futures in 1976/1977 as a point of reference.

Initially, Treasury futures (and a sister GNMA future) were not a success. When they finally launched, volumes were only a few hundred contracts per day.

Low volume on this new contract is not a surprise, given the “when issued” impact of the CDS future and the effect on final settlement. Many counterparties may be on the sidelines until the new contract goes through the first final settlement.

Is Dodd-Frank & EMIR Equal To The End of Britton Woods?

OTC swaps and specifically CDS’ do not have a “Nixon Shock/end of gold standard” fracture with currency and inflationary volatility. These waves of volatility served as the “tipping point” for currency and interest rate futures. Like the currency markets of the early 1970’s, OTC CDS swaps are a custom forward market. Thus, the CDS future is in many ways similar to currency futures as both vehicles create standard models for the exchange of counterparty risk.

As market forces will not drive the adoption of a CDS future, will regulation and regulatory pressures do it? Title VII of Dodd Frank changed the execution, clearing, and capital structures of the CDS swap market. Let’s take a look at changes in the OTC market as compared to the ICE futures. First and foremost, margin and transaction costs are now highest for customized products and lowest for standardized exchange-traded products.

Category

Bilateral Swaps

OTC Cleared

Futures

Liquidity

Liquid

Highest Liquidity

Illiquid

Margin

Highest, TBD

Higher

Lowest

Margin Calculations

10-day VaR

5-day VaR

2-day VaR

Transaction Costs

Basel III Capital Requirements

FCM and associated LSOC costs

FCM Margin/Cost of Carry

Termination/Compression

Intra-party and Compression

Intra-party and Compression

Exchange

Valuation and Reporting

Intra-party

Intra-party & SEF

Exchange

Dodd Frank, EMIR, and Basel III changed the rules but internal momentum is still hard to overcome. Any OTC CDS futures development must be co-dependent with the existing OTC swap market. Exchanges are the ultimate networked organization where liquidity begets liquidity. Therefore, for the futures to gain liquidity, the following Tipping Point actions must occur simultaneously.

Outstanding rules and margins for non-cleared bi-lateral swaps must be completed and implemented. This includes implementation of Category 3 participants in centralized swap clearing.

Banks must decide (or be convinced as part of increased CDS market scrutiny) to utilize CDS futures. This benefits banks on the Basel III capital requirements side and the overall CDS market in terms of transparency.

For parties interested in hedging credit risk on a macro-level, substitution value of the CDS future must be greater than the disincentive of a new, riskier product. In other words, the perceived opportunity cost of trading CDS futures is very high.

June 2013 was a volatile period. Volatility was not just related to central bank intervention and QEIII discussions. Operational volatility in the OTC market was the result of new rules and margins for Category 1 and 2 participants. In hindsight, given the confusion around central clearing, market participants are still trying to adjust to the fluidity of new swap rules rather than eyeing a complementary credit risk solution.

In this context, it is important to not underestimate the gravitational pull of the existing swap market. Traders, banks, and counterparties have underlying relationships with swap desks (and not necessarily with a futures counterpart). Transacting with two desks lowers your relative importance in a market that depends on relationships.

So, can this pig sing? Well, we don’t know yet. Once the music director finishes writing the final score and the band makes an entrance, then we’ll know whether this little piggy has a market or this little piggy gets none.

Amara’s Law states we overestimate a technology’s effect in the short-run and underestimate it in the long run. This is happening with the proliferation of “big data” announcements in the capital markets space. Big Data’s quasi-definition currently relates to identifying patterns in unstructured data. The best example is that of Sentiment Analysis (twitter feeds, social media, etc..) being cited as a price movement indicator. This model of ‘big data’ is only a short-term arbitrage opportunity.

Sentiment Analysis is a small step in the right direction, but the big money to be made will come with the monitoring of embedded devices. This concept is called the “Internet of Things” (or IoT). Although I find it optimistic, some groups have predicted 30 billion devices will be monitored via the Internet in the next 7 years. As the number of inter-connected devices continues to skyrocket, the winners will be those that can quickly interpret the terabytes of unstructed data.

Why will this have a dramatic effect on capital markets? Take the Sentiment Analysis example and apply this to a world of connected devices. Let’s apply this to a market like gasoline futures. Instead of waiting for the US Energy Department to release the weekly inventory report, unstructured pump-level data is now sent across the internet from 10,000 gas stations and 50 refineries. The data is constantly collected and analyzed using deep visualization techniques. A data analyst and modeler look at real-time consumption trends for arbitrage opportunities and create a algorithm to predict future-state consumption patterns. This algorithm is now used utilized to trade futures and equities (oil refiners, agricultural producers, etc..) and fed into a larger algorithm for retail consumption. Trading and investment now becomes a command and control activity of monitoring activity and adjusting algorithms for non-quantitative factors.

Unstructured data is a key predictor of future economic activity. No longer will analysts need to rely on qualitative sentiment indicators like Manufacturing Purchasing Index and Consumer Confidence that claim to be forward indicator (but are really emotional glances at the rear-view mirror.) In an IoT world, analysts use aggregated data to identify real-world trends. ( Note: Like discussions with with my local Costco manager who noted that in late 2007 consumers had moved heavily into buying basis foodstuffs [eggs, fruit] and away from higher-end items such as furniture and electronics).

Big Data may also create mini-futures and micro-exchanges via commoditization of goods and services. If you have the infrastructure to reliable monitor and predict supply and demand of goods and services, then providing a mini-contract helps eliminate risk. Providing a method for hedge funds and/or traders to serve as risk brokers via buyers and sellers of a good or services helps overcome asymetric information. Previously, only inside buyers and sellers in a market had this information. Unstructured data would allow other risk takers to enter a market and help in price discovery and long term market stability.

Like the title mentions, we have seen very little of the potential of Big Data analysis in the capital markets….but it is coming and sooner than you think.

There are many reasons to lean into the Kinetic Investment Environment but the most compelling reason is differentiation. This environment complements your most important resource, People, and challenges them to build a creative investment environment. The Kinetic Environment also fits with more stringent operational and risk protocols via templates and heatmaps.

The Path To A Kinetic Environment – Leveraging Existing Infrastructure

While the quickest path to a Kinetic Environment is the replacement of existing tools and technologies, most firms are not in a position to decommission large swaths of investment technology. The good news is that existing investments can be leveraged with modest outlays. Most firms have a solid foundation in database and data warehouse technologies. These can easily be integrated with advanced analytics and dashboard technology.

From 2013 onwards

In the age of information arbitrage, simplification is power. This simplification is achieved visually. The investment team evolves to include financial modeling, data, and content skills. The end product is a cohesive rule-based investment architecture that is theme-dependent and managed by a highly motivated, interactive team. It’s not the 1980’s anymore. It’s time to align the investment environment with the needs of modern information arbitrage.

To understand the Kinetic Investment Environment, you have to view an investment environment in terms of the 4P’s. Let’s start out with the first P, Performance. Performance in a kinetic environment is all about creating the biggest brain possible. A multi-sensory interactive Trade Room serves as the eyes and executive center of an investment environment. The room is one big tactile, visual display. Wave your hand, point to a ticker, and point to the wall. The latest economic news and tick data is posted to the trade wall. The more brains the better as the Trade Room is designed to leverage a team’s collective brain power.

The second P, Process, is all about the visual engineering of a repeatable and rigorous process. Visual engineering uses advancements in data warehouses, analytic engines, and visual dashboards. Need to understand risk in a portfolio….pull up a color-coded heatmap showing Component VaR. Want to understand how a change in interest rates affects dollar duration? Pull up a 3-D visual model of simulated yield curve. If a portfolio position starts to blink red, drill-down into details about correlation.

Changes are not just about the power of visual dashboards. It’s about using tools in a consistent manner. Templates are mandatory during the evaluation of a new investment and serve as the basis for ongoing portfolio management. Because the templates are objects, underlying data, algorithm code, and analytics are stored together in a trade data warehouse. Meta-data is also maintained and searchable so that trade objects can be referenced, duplicated, and stress-tested.

The third and most important P is for People. People generate, evaluate, synthesize, and curate investment ideas. The old school need for only PMs and Investment Analysts on a team is dead. In a kinetic environment, financial analysis is complemented by pattern analysis across massive data streams and combined into visual analytics. This new investment team includes skills in data analysis, modeling, simulation, and content presentation. Risk and compliance are incorporated as front-end activity via visual dashboards.

The last P of the Kinetic Environment is Philosophy. Although complex technologies are used, the philosophy is simple. It’s about telling a good story. The people and technology translate collective data points into a cohesive visual story. The story can be recited from analyst to CIO and from investment committee to investor.

1980 was a year of momentous changes for Wall Street investment firms and trading desks. A spreadsheet app named Visicalc was creating “Screen Envy” across firms as early adopters created a buzz with the analytical prowess of personal computing. The U.S. federal deficit was $900B. Dallas was the top-rated television show. The Star Wars sequel, The Empire Strikes Back, captured the minds and wallets of audiences worldwide.

Fast forward thirty-three years. The U.S. federal deficit is $17T. Dallas’s next generation is back on TV and five additional Star Wars movies have been released. So much has changed. However, the basic look and feel of an investment environment remains as it did in the early 1980’s…..until today.

We are now at flex point that can shape the 4P’s (performance, process, people, and philosophy) for the next generation of trading environments. This intersection accounts for the evolution of computer technology, big data, the human-computing interface, and algorithmic trading. I call this vision the Kinetic Investment Environment.